From 56a95f207b33b5ef783bc01c2b7038dc4c3a6093 Mon Sep 17 00:00:00 2001 From: dong <2938690875@qq.com> Date: Sun, 3 Nov 2024 14:41:15 +0800 Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- function.py | 39 +++++++++++++++++---------------------- 1 file changed, 17 insertions(+), 22 deletions(-) diff --git a/function.py b/function.py index f9e4489..84439ba 100644 --- a/function.py +++ b/function.py @@ -1,5 +1,6 @@ -import numpy as np + import numpy as np import os +from sklearn.preprocessing import StandardScaler # 导入标准化库 # 学号和姓名 id = "202210423130" @@ -45,7 +46,7 @@ def predict_logreg(inX, weights): h = sigmoid(z) return 1 if h >= 0.5 else 0 -def load_data(filepath, remove_indices=None): +def load_data(filepath): """从文件中加载数据。""" if not os.path.exists(filepath): raise FileNotFoundError(f"文件 {filepath} 不存在！") @@ -53,10 +54,8 @@ def load_data(filepath, remove_indices=None): data, labels = [], [] print(f"加载数据中: {filepath}") with open(filepath, 'r') as f: - for line_idx, line in enumerate(f): + for line in f: line = line.strip().split() - if line_idx == 2: # 跳过第三行 - continue if len(line) >= 1 and line[-1].isdigit(): line_data = [0 if x == '?' else float(x) for x in line[:-1]] data.append(line_data) @@ -65,20 +64,18 @@ def load_data(filepath, remove_indices=None): data = np.array(data) labels = np.array(labels) - if remove_indices is not None: - data = np.delete(data, remove_indices, axis=1) + num_features = data.shape[1] + indices_to_remove = [2] # 去掉特征3（索引2） + + if num_features > 23: + indices_to_remove.extend(range(23, min(28, num_features))) + data = np.delete(data, indices_to_remove, axis=1) target_variable = np.where((labels == 2) | (labels == 3), 0, 1) print(f"数据加载成功，数据形状: {data.shape}, 标签形状: {target_variable.shape}") return data, target_variable -def normalize_data(X): - """归一化数据。""" - min_vals = X.min(axis=0) - max_vals = X.max(axis=0) - return (X - min_vals) / (max_vals - min_vals) - def k_fold_cross_validation(X, y, k=5, learning_rate=0.001, iterations=10000, batch_size=64, lambda_reg=0.1): """K折交叉验证。""" m = X.shape[0] @@ -104,18 +101,16 @@ def k_fold_cross_validation(X, y, k=5, learning_rate=0.001, iterations=10000, ba return np.mean(accuracies) if __name__ == "__main__": - # 剔除特征索引 - indices_to_remove = [2] # 去掉特征3（索引2） - # 加载训练集 try: - X_train, y_train = load_data("./data/horse-colic.data", remove_indices=indices_to_remove) + X_train, y_train = load_data("./data/horse-colic.data") except FileNotFoundError as e: print(e) exit() - # 归一化训练集 - X_train = normalize_data(X_train) + # 数据归一化处理 + scaler = StandardScaler() + X_train = scaler.fit_transform(X_train) # 使用训练集进行标准化 # k折交叉验证 avg_accuracy = k_fold_cross_validation(X_train, y_train, k=5, learning_rate=0.01, iterations=1000) @@ -123,13 +118,13 @@ if __name__ == "__main__": # 加载测试集 try: - X_test, y_test = load_data("./data/horse-colic.test", remove_indices=indices_to_remove) + X_test, y_test = load_data("./data/horse-colic.test") except FileNotFoundError as e: print(e) exit() - # 归一化测试集 - X_test = normalize_data(X_test) + # 测试集也需要进行相同的归一化 + X_test = scaler.transform(X_test) # 使用相同的Scaler进行测试集的标准化 # 训练对率回归模型 weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000)